Exploring a Datawarehouse

At times, business processes of any market scenario demands a data warehouse for its improvement. Targeted to align and conform the entire data at 'One place', these data warehouses, are needful in the areas of trend identification, situations leading to variabilities and many more.

In general, enterprises that start up to install a data warehouse for a process oriented data have drawn up to an end requiring two separate warehouses – A classic warehouse to reach the needs of a discrete data that is maintained mostly by the IT departments.
And a historian data warehouse handled by the engineering department, to keep hold of continuous data.

However, situations may arise, wherein users require both types of data to meet specific requirements that bring together these two types of data. Faced with the increased mandates in the process knowledge areas and also the demand for the needs to access data from any manufacturing sources like enterprise analyzers, an increasing percentage of drug industry professionals find that traditional warehouses alone may not make sure to enable an Enterprise Manufacturing Intelligence -EMI.

Situations may arise where they add more price or may slow down at data access. Over the other edge, it may be a cost effective and also the required ETL - Extract/Transfer/Load processes may be expensive in order to develop and maintain a traditional data warehouse.

In addition, users of these traditional data warehouses need to define the context of the required information, that has a probability of leading to a specific hurdle in bio-tech, considering for example scenarios where splits and recombinations result at a tentative genealogy conditions.

An alternative to all such stated conditions can be found when observed at an Enterprise Manufacturing Intelligence(EMI) software where the systems are driven by a data access that is of lightweight , combined platform and having a dynamic mapping engine (DME) in place, that brings together single users nearer to the data they need for the interpretation and narrating as part of day to day activity. Lets explore more in these lines from our experts desk in the upcoming articles.

Datawarehousing and decision making

Earlier articles explored more in lines of data warehousing for various domains and needs of data warehousing at different levels of an enterprise. Lets see the evolving methodologies of data warehousing ensures the pharmaceutical enterprises for its research with an effective decision making.

Any research and development activity in a pharmaceutical enterprise involves a particular type of information and makes optimum utilization of the available data and keeps an insight into a huge set of decision making scenarios along its path.

A clinical data repository seems to be continuously updated with relational data warehouse that facilitates its users with direct access to detailed, flexible, and rapid related views of clinical, administrative and financial patient data in times of changes in the planned processes and addition of new requirements and application modules.

Having Data warehousing into implementation ,drug research process can be improvised and results can be obtained in a short span with the integration of the different data marts from various sources . It further helps in the successful research of the drug for the pharma enterprises.

By analyzing the processes and data marts from the Research and Development of a drug in a pharmaceutical case study , the key types of information sources that have been utilized either internally or externally can make major decision-making .

A classification can be obtained within the information systems of the data warehousing and further levels can be determined for decision making. Lets find more information in these lines from our research desk in a later article.

Effective utilization of data marts

Earlier articles of Data Warehousing ended up with an insight into the importance of data warehousing in the present market and its effective services as per demand. It evolved as a key player in various domains say for example pharmaceutical industries.

From the very basic understanding of data warehousing , it states that data warehousing is similar to a database or a data store in which the data from various sources of the enterprise is collected and organized in an efficient pattern so as to make the data available for further analysis and reporting systems.

In general a data warehouse is a subject-oriented, structured and time-variant solicitation of data that is processed for the support of an enterprises management's decisions , within which a data mart is a collection of data for a related group or for a particular department.

Lets further proceed towards the importance of data marts and its effective utilization in a data warehouse where Data marts when merged with each other or even if integrated with one another in an enterprise Data Warehousing, supports in a wider reach of corporate data.

A competent based model of a data mart or a shared service of a data mart helps in maximum installations that makes the administration of the Data Warehouse components easy. Designing a data mart according to a defined strategy helps it in meeting an immediate need.

When managed as a repository of collected data from various sources and departments, a Data mart can be designed further to knowledge transfer the available data to the resources of an enterprise on a need basis.

Emphasizing the data mart makes it in a way so as to meet particular needs of specific group of knowledge users in key situations like analyzing, presenting and manage the data content with an ease. Lets explore more on data mart and its consolidation in next article.